The Impact of Pain Invisibility on Patient-Centered Care and Empathetic Attitude in Chronic Pain Management
Bibliographic record
Abstract
Objectives. The use of interdisciplinary patient-centered care (PCC) and empathetic behaviour seems to be a promising avenue to address chronic pain management, but their use in this context seems to be suboptimal. Several patient factors can influence the use of PCC and empathy, but little is known about the impact of pain visibility on these behaviours. The objective of this study was to investigate the influence of visible physical signs on caregiver’s patient-centered and empathetic behaviours in chronic pain context. Methods. A convenience sample of 21 nurses and 21 physicians participated in a descriptive study. PCC and empathy were evaluated from self-assessment and observer’s assessment using a video of real patients with chronic pain. Results. The results show that caregivers have demonstrated an intraindividual variability: PCC and empathetic behaviours of the participants were significantly higher for patients who have visible signs of pain (rheumatoid arthritis and complex regional pain syndrome) than for those who have no visible signs (Ehler–Danlos syndrome and fibromyalgia) ( p<0.001 ). Participants who show a greater difference in their patient-centered behaviour according to pain visibility have less clinical experience. Discussion. The pain visibility in chronic pain patients is an important factor contributing to an increased use of PCC and empathy by nurses and physicians, and clinical experience can influence their behaviours. Thus, pain invisibility can be a barrier to quality of care, and these findings reinforce the relevance to educating caregivers to these unconscious biases on their behaviour toward chronic pain patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".